The present disclosure relates to systems and methods for magnetic resonance imaging (“MRI”). More particularly, the disclosure relates to systems and methods for producing quantitative parameter maps using magnetic resonance fingerprinting (“MRF”).
MRF is an imaging technique that enables quantitative mapping of tissue or other material properties based on random or pseudorandom measurements of the subject or object being imaged. Examples of parameters that can be mapped include longitudinal relaxation time, T1; transverse relaxation time, T2; main magnetic field map, B0; and proton density, ρ. MRF is generally described in U.S. Pat. No. 8,723,518, which is herein incorporated by reference in its entirety.
The random or pseudorandom measurements obtained in MRF techniques are achieved by varying the acquisition parameters from one repetition time (“TR”) period to the next, which creates a time series of images with varying contrast. Examples of acquisition parameters that can be varied include flip angle, radio frequency (“RF”) pulse phase, TR, echo time (“TE”), and sampling patterns, such as by modifying one or more readout encoding gradients.
The data acquired with MRF techniques are compared with a dictionary of signal models, or templates, that have been generated for different acquisition parameters from magnetic resonance signal models, such as Bloch equation-based physics simulations. This comparison allows estimation of the desired physical parameters, such as those mentioned above. The parameters for the tissue or other material in a given voxel are estimated to be the values that provide the best signal template matching.
Quantitative parameters are estimated in MRF by matching acquired signals with pre-computed signals that are stored in a dictionary of tissue parameters. To ensure that a correct match is found for each acquired signal, the dictionary must cover a large range of tissue parameters and must have a fine resolution. These two requirements result in dictionaries that are large (e.g., on the order of one million entries). As a consequence of the large dictionary size, significant processing time is required to match acquired signals to the dictionary, and large storage space is needed to store the dictionaries.
Thus, there remains a need to provide systems and methods for MRF that are more computationally efficient, and have reduced data storage requirements.